MathWorks - Advanced Deep Learning Techniques for Computer Vision
- Offered byCoursera
- Public/Government Institute
Advanced Deep Learning Techniques for Computer Vision at Coursera Overview
Duration | 7 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Advanced Deep Learning Techniques for Computer Vision at Coursera Highlights
- Earn a certificate from Coursera
- Learn from industry experts
Advanced Deep Learning Techniques for Computer Vision at Coursera Course details
- Train and calibrate specialized models known as anomaly detectors
- Generate synthetic training images for situations where acquiring more data is expensive or impossible
- Use AI-assisted auto-labeling to save time and money
- Import models from 3rd party tools like PyTorch and export your model outside of MATLAB
- Visual inspection and medical imaging are two applications that aim to find anything unusual in images
- In this course, you'll train and calibrate specialized models known as anomaly detectors to identify defects
- You'll also use advanced techniques to overcome common data challenges with deep learning
- AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images
Advanced Deep Learning Techniques for Computer Vision at Coursera Curriculum
Anomaly Detection
Deep Learning for Computer Vision
Advanced Deep Learning Techniques for Computer Vision
Detecting Anomalies
Detecting Anomalies in MATLAB
Meet Your Instructors
Course files and MATLAB
Installing Pre-Trained Models
PCB Anomaly Detection with PatchCore and FCDD
Introduction to the Assessment
Concept Check: Anomaly Detection
Graded Quiz: Detecting Anomalies in Endoscopy Images
Data Augmentation
Introduction to Data Augmentation
Data Augmentation for Object Detection
Data Augmentation for Classification
Data Augmentation Quick Reference
Data Augmentation for Object Detection
Example of Augmentation Improving Performance
Concept Check: Data Augmentation
Graded Quiz: Data Augmentation
Model-Assisted Labeling
Model-Assisted Labeling
Fasteners Automation Function
Introduction to Parking Image Labeling Project
Parking Labeling Project
Concept Check: Model-Assisted Labeling
Graded Quiz: Summarizing Labeling Results
Creating Your Own Models
Starting Your Own Deep Learning Project
Working with Third Party Models
Integrating Your Code
Summary of Deep Learning for Computer Vision
Deep Learning Workflow Reference
Importing and Exporting from Third Party Platforms
Deploying Your Model
Further Enhance Your Skills
Graded Quiz: Creating Your Own Models
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